Best Local Transcription Apps for Mac and Windows in 2026
Audio Note, MacWhisper, Superwhisper, whisper.cpp, and built-in dictation compared by job, with our public model benchmark and a twenty-minute self-test.
The short answer
Four jobs cover most people searching for this:
- A recording goes in, a transcript or subtitle file comes out. On a Mac, MacWhisper is the most complete option, with a one-time Pro license.
- You talk, and text lands in whatever app you are typing in. Dictation, not transcription. Superwhisper does it on Mac, Windows, and phones; VoiceInk is the open-source option (Mac only).
- Meetings or system audio need to be captured live, then turned into notes. MacWhisper and Superwhisper now record meetings on the Mac too; on Windows, or when you also need watch folders and link transcription, Audio Note fits better. We make it, so the disclosure below applies.
- You script things or process batches of files. whisper.cpp or the OpenAI Whisper CLI, no GUI.
If you want the reasoning behind those lines, read on. If you want a number before you trust any of it, skip to the benchmark section; the raw data is downloadable.
How this page was put together
We make Audio Note, and every comparison that includes the author's own product carries that conflict, including this one and the nine other comparison pages we read while writing it. Three things we did about it.
Competitor entries list only capabilities checkable on their public pages. No prices, because prices change and we cannot re-verify them monthly. No accuracy claims, because none of the tools publish their test data.
The accuracy section is not about competitors at all. It covers eight offline ASR models measured on the same five clips, with raw JSON published so you can recompute every number yourself.
And at the end there is a test protocol. Twenty minutes with your own audio settles more than any table on this page.
How to judge a local transcription app
Before looking at specific tools, settle on the criteria:
- Data boundary: does core transcription run on your machine, and do audio and transcripts stay there?
- Accuracy: how does it do on your languages, including mixed speech, and can you verify it yourself?
- Speed: how long a file takes, and whether live latency is acceptable.
- Setup: command line or not, manual model downloads or not.
- After the transcript: summaries, subtitle export, and batch automation.
Every judgment below comes back to these five.
What "local" actually covers
Most comparisons treat "local" as one switch. There are two, and they fail separately.
Core transcription is the first one: audio in, text out. For every tool on this page, that step runs on your machine.
Everything around it is the second one: model downloads, license checks, updates, and any AI summary feature. These vary. AI summaries in Audio Note can use a cloud model or a local one, so a meeting that cannot leave the room can still be summarized, if you point the AI at a local backend. Some dictation apps offer cloud models as an upgrade you opt into. Commercial apps generally check licenses online, which sends account metadata, not audio.
If your policy is "zero network, ever", do not take any vendor's word for it, ours included. Route the app through a firewall like Little Snitch or GlassWire, run one file, and watch what leaves. That check takes about a minute.
The comparison
| Tool | Platforms | Files and batch | Live mic | App or system audio | Watch folders | Speaker labels | SRT/VTT export | Transcript to notes |
|---|---|---|---|---|---|---|---|---|
| Audio Note | macOS, Windows | Yes | Yes (Standard and up) | Yes (Standard and up) | Yes | Not yet (in development) | SRT, VTT, ASS and 8 more | AI summaries, action items, notes |
| MacWhisper | macOS | Yes | Yes | Yes (meetings, app audio) | Yes (Pro, beta) | Yes (Pro) | SRT, VTT | AI summaries and chat, local or cloud models |
| Superwhisper | macOS, Windows, iOS, Android | Yes (Pro) | Yes (realtime is Pro) | Yes (Meetings Mac-only; system audio is Pro) | Not mentioned | Yes (Pro) | Not mentioned | Text at the cursor, meeting summaries |
| whisper.cpp / Whisper CLI | macOS, Windows, Linux | Yes | Possible with custom code | Possible with custom code | Script it | Add a diarization model yourself | Possible | Build it |
| macOS / Windows dictation | Built in | No | Yes | No | No | No | No | No |
| Cloud notetakers (Otter, Fireflies) | Web, desktop, mobile | Partial | Yes | Meeting bot, or botless desktop recording | No | Yes (not in Fireflies' botless desktop mode) | Partial | Yes |
Competitor details last verified October 2026 against the MacWhisper site, the Superwhisper site and plans page, Otter, and the Fireflies desktop guide. Features and plans change; check before buying.
A few entries need context. Many MacWhisper capabilities (speaker separation, watch folders, link transcription, live captions) are in Pro. Superwhisper's Meetings is Mac-only for now, and file transcription, system audio recording, and speaker separation need Pro. Cloud notetakers process audio on their servers whether they use a bot or a desktop recorder, which is why they appear in a local comparison only as the thing you are choosing against. With whisper.cpp you would bolt on a separate diarization model.
In Audio Note, vocal separation and voice enhancement are Pro and Lifetime features. Speaker diarization is still in development and not available yet. The free plan covers Whisper Tiny through Small on CPU only. See the pricing page for the full plan breakdown.
Who each tool is for
Audio Note: meetings, system audio, and batches of files
Pick it when the audio itself is your work material. File transcription, live microphone, app audio, global realtime (beta), link transcription, and watch folders live in one app, and the AI assistant turns the result into minutes, subtitles, or Markdown.
Good fit if you:
- run online meetings every day and need a record fast
- have strict privacy requirements, as lawyers, clinicians, and journalists often do
- do not want a command line and are fine downloading a model once
- want the transcript to keep going into notes and summaries
Know the limits: the first run needs a model download; live transcription and GPU acceleration start at the Standard plan; speaker separation is still in development; dictation is a v2.2.0 beta; system and app audio permissions work differently on macOS and Windows.
MacWhisper: the full-featured one-time purchase on Mac
Mac only, and you want one app that does it all: files, meetings, speaker separation, watch folders, link transcription, and system-wide dictation, mostly in Pro, which is a one-time license. If you need automatic speaker labels today, it fits better than Audio Note. The trade-off is that it is Mac only.
Superwhisper: dictation first, phones included
You write email, tickets, and docs by talking, the text needs to land in Mail, VS Code, or a browser, and you want the same setup on Windows, iPhone, and Android. It also records meetings with speaker separation, but Meetings is Mac-only for now and file transcription needs Pro. Pricing is a subscription or a lifetime license.
Built-in dictation: free, inflexible
macOS and Windows both ship dictation for short input. It cannot process recorded files, is not built for batches or automation, and exports no subtitles or timestamps. Whether it runs on device depends on your system settings and the vendor's privacy documentation.
Whisper CLI: for developers
Free, open source, full control over models and parameters. The cost: setting up Python or compiling whisper.cpp, handling GPU acceleration yourself, and building any post-processing. Good if you live in a terminal or want transcription inside your own project.
What the numbers everyone copies actually mean
Open enough comparison pages and you will meet the same table: Whisper tiny at roughly 7.6% word error rate, base around 5.8%, small around 4.2%, large around 2.7%. We saw that table or fragments of it on several of the nine pages we read while researching this one. Not one of them linked a source or said what audio produced the numbers.
Those figures come from clean English read-speech benchmarks. Your meeting audio is none of that. The numbers are not wrong; they are answering a question nobody asked.
So instead of repeating a table we cannot verify, we ran our own. Details below.
Our own run: eight models plus Whisper, five clips, public JSON
We transcribed the same five clips (four English, one Chinese, 34 to 80 seconds each) with eight offline ASR models, on a Mac running the ML engine on CPU and on a Windows box (Ryzen Threadripper 3960X, CPU, 8 threads). Whisper numbers come from a separate whisper.cpp run on the Windows machine's RTX 3080. Raw files, with per-clip results:
Percentages are WER for English and CER for Chinese. RTF is transcribe time divided by audio length, so 0.019 means an hour of audio finishes in about 68 seconds.
| Model | English WER, Mac / Win | Chinese CER | RTF, Mac / Win |
|---|---|---|---|
| Qwen3-ASR | 1.19 / 1.59 | 0.65 | 0.227 / 0.302 |
| Parakeet TDT | 1.71 / 1.59 | not tested | 0.103 / 0.170 |
| SenseVoice | 3.13 / 3.15 | 1.61 | 0.044 / 0.061 |
| Moonshine | 4.04 / 4.04 | not tested | 0.019 / 0.031 |
| Cohere Transcribe (multilingual) | 1.78 / 1.78 | 10.32 | 0.346 / 0.365 |
| FireRed CTC | 11.42 / 11.84 | 2.58 / 3.23 | 0.216 / 0.277 |
| Zipformer (English) | 8.81 / 8.81 | not tested | 0.038 / 0.090 |
| Zipformer (Chinese-English) | 9.85 / 9.87 | 2.26 / 3.23 | 0.029 / 0.067 |
| Whisper Small (whisper.cpp, Win GPU) | 1.76 (CUDA) | 0.97 | 0.062 |
Three things this table shows that the recycled one does not.
Match the metric to the language. Cohere Transcribe is excellent on our English clips and more than six times worse on the Chinese one: CER 10.32% against SenseVoice's 1.61%. A "multilingual" label does not tell you which languages survived. If you transcribe Chinese, look at CER on Chinese audio, not WER on English benchmarks.
Speed and accuracy are separate dials. WER barely moved between our Mac and the Windows machine. RTF did: Moonshine ran about 1.7x slower on Windows, the English Zipformer 2.4x. And on the same Windows box, whisper.cpp's Base model hit 2.40% WER on both CUDA and Vulkan, while RTF went 0.047 versus 0.112. Changing the GPU backend changed the wait, not the text.
Small does not mean bad. Whisper Base is a 150 MB download and it beat several larger models on our clips, partly because most of our clips are clear synthetic speech, easier than real meetings. If your audio is a clear voice on a decent mic, you may not need the 3 GB model at all. On a phone recording across a table, even the biggest will struggle, and no table predicts by how much.
Five clips is not a leaderboard. No accents, no far-field meetings, no phone audio, CPU only for the ML engine run. Use these numbers to reason about mechanisms, not to rank models for your audio.
Chinese and mixed-language audio
Most English comparison pages do not mention Chinese at all, which is a strange gap in 2026. Two practical notes from our run.
Pick by CER, not brand. SenseVoice gives up a little accuracy against Qwen3-ASR (1.61 vs 0.65 CER) and runs about five times faster. For Chinese interviews you will edit anyway, the slower model rarely pays for itself. Qwen3 earns its runtime when wording has to be tight.
Mixed Chinese-English speech needs its own test. Code-switching breaks language detection assumptions in some models, and our fixture set has only one Chinese clip, so we will say it plainly: we do not have good data here yet, and neither does anyone else publishing comparisons. Run a sample of your own mixed audio through any tool before committing.
A twenty-minute test before you commit
Whatever you buy, this protocol beats every table on the internet, this one included.
Pick three clips: a normal meeting, a noisy or far-field one, and your worst audio, maybe a phone voice memo or two people talking over each other.
Run each through the two tools still in play, default settings, and note which model each used. Then count corrections in the first minute of output by hand. Hand-counting WER on sixty seconds is close enough for a buying decision.
Time a full file and divide by its length. That is your RTF on your machine. Under 0.1 means an hour of audio takes under six minutes.
If privacy drove this decision, run the firewall check from the section above while the file processes.
Who should not go local
Local transcription is the right answer for most of the people who search for it, and the wrong one for a few cases worth naming.
If your audio is phone recordings or conference calls recorded through a phone bridge, expect worse results from every local tool. Test before buying anything.
If you need transcripts with zero setup on a machine you do not control, a web service may genuinely fit better. Local tools want to install a model at least once.
If you need publishable accuracy without proofreading, no tool gets you there, local or cloud; human transcription services still exist for a reason.
FAQ
Is local transcription as accurate as cloud services? On clean English audio, the gap has closed to the point where it usually does not drive the decision. Our own clips put several local models under 2% WER. On hard audio, cloud preprocessing sometimes still wins, but you should test with your own recordings either way, because published numbers do not transfer.
Does local transcription work with no internet at all? Core transcription does, after the model is downloaded once. Updates, license checks, and optional AI features may still touch the network. If that matters, verify with a firewall instead of trusting the product page.
What hardware do I need? Small Whisper models run on any machine from the last several years. GPU acceleration matters for large models and long files; on our Windows box, CUDA cut whisper.cpp Base processing time by more than half versus Vulkan, with identical accuracy.
Which model should I start with? For file transcription, Small is a reasonable default; step up to Medium or Large-v3 Turbo when accuracy matters. For Chinese, try SenseVoice. For live capture, realtime models trade a little accuracy for much lower latency. See model selection tips and the models overview in the docs.
Can these tools handle Chinese? Several local models do Chinese well; SenseVoice and Qwen3-ASR both scored under 2% CER on our Chinese clip. The risk is not Chinese, it is mixing: test Chinese-English code-switching specifically.
What does local transcription cost? whisper.cpp and the Whisper CLI are free. MacWhisper has a free tier plus a one-time Pro license; Superwhisper has a free tier plus a Pro subscription or lifetime license. Check their sites for current prices. Audio Note has a free plan (Whisper Tiny to Small, CPU only, 1 GB per file); current paid plan prices and features are on the pricing page.
Where to go from here
Related guides on this site:
- Why local transcription is safer
- Audio Note vs MacWhisper vs Superwhisper
- Transcribe Zoom, Teams, and Meet locally
- Subtitle formats guide
If the short answer section matched your job, the fastest way to decide is still the twenty-minute test on your own audio. Audio Note for Mac and Windows is here if that test lands on us.
